Identifying IT impacts on organizational structure …

1 Identifying IT impacts on organizational structure and business value Pia Gustafsson1, Ulrik Franke1, Pontus Johnson1, Joakim Lilliesk ld1, 1 Royal Institute of Technology, Industrial Information and Control Systems, Osquldas , SE-100 44 Stockholm, Sweden {piag, ulrikf, pj101, Abstract. This paper presents a framework for analysis of how IT systems add business value by causally affecting the structuring of organizations. To aid our understanding of IT benefits related to organizational structure , we put the well established theory of organizational behavior developed by Mintzberg to use. Combining Mintzberg with more recent research on the business value of IT, the result is a qualitative multi-disciplinary theoretical framework that shows which business values are affected by IT in relation to the organizational structure .}

2 This framework can be used to analyze what kind of IT system should be used by an organization with a given structure to maximize its business value . Keywords: IT benefits, organizational structure , Mintzberg, business value 1 Introduction It has long been discussed in the IT value research area whether IT adds value to an organization or not. Following Brynjolfsson [1], the discussion in the literature increasingly supports the theory that IT can add business value to an organization. For instance, Bergsj et al [2] have shown that the user satisfaction caused by functionality, usability, information structure etc. affects the quality, efficiency and innovations of IT users. Researchers (and practitioners) now turn focus to the question of how IT adds value to the organization [3].

3 This problem is approached here by an attempt to combine the traditional theory of organizational structures with more recent research on how aspects of IT might affect the structure or the workings of the organization. Dahlgren [4] stresses that organizational structure has a defining role on how information flows within an organization and, as a consequence, how well processes are performed and resources are spent. Other studies of the impact of electronic communication systems on business organizations are Fulk et al., [5], Andersen [6,] and Gurbaxani et al. [7]. Traditional organizational theory describes organizations; the behavior of groups of people in them, how strategies and structures influence the groups, how the organizations suit different purposes and how they can be managed to achieve goals.

4 Research on the business value of IT, often within the enterprise architecture research paradigm, tends to focus on the relation between various information systems Proceedings of BUSITAL 08 45 Outline The remainder of this paper is structured as follows. Extended influence diagrams used for causal modeling are introduced in section 2. Section 3 presents the framework of business values used, in the shape of an extended influence diagram. Section 4 connects the organizational theory of Mintzberg [8] to these business values. In section 5 the extended influence diagram is further extended to include the connections to IT. The applicability of the metamodel is discussed in the subsequent section 6.

5 Section 7 concludes the paper. 2 Extended Influence Diagrams Extended influence diagrams (EID) are graphic representations of decision problems coupled with a probabilistic inference engine. These diagrams may be used to formally specify enterprise architecture analysis [9]. The diagrams are an extension of influence diagrams, as described by Shachter [10, 11] which in turn are an enhancement of Bayesian networks (cf. Neapolitan [12] and Jensen [13]). In extended influence diagrams, random variables graphically represented as chance nodes may assume values, or states, from a finite domain (cf. Fig. 1). A utility node could for example be organizational performance . The utility node could be further described by other nodes that it has a definitional relation to.

6 Causal relations capture associations of the real world, such as an automation system affects the process efficiency . In Fig. 1, this is visualized by Scenario Selection that causally affects the Process efficiency which itself causally affects the organizational performance . Fig. 1. An extended influence diagram and a simple example Extended influence diagrams support probabilistic inference in the same manner as Bayesian networks do; given the value of one node, the values of related nodes can be calculated. With the help of a conditional probability table (CPT) for a certain variable A and knowledge of the current states of the causally influencing variables B and C, it is possible to infer the likelihood of node A assuming any of its states.

7 With 46 Proceedings of BUSITAL 08 a chosen scenario, the chance nodes will assume different values, thereby influencing the utility node. For more comprehensive treatments on influence diagrams and extended influence diagrams see Johnson et al. [9], Shachter [10, 11], Neapolitan [12], Jensen [13], and Johnson et al [14]. However powerful a research tool the EID framework is, EID:s cannot be created ex nihilo. There exists a lot of research on how to elicit the quantitative estimates used to create CPTs, for example Druzdzel et al. [15] and Keeney et al. [16]. Nevertheless, these methods are applicable only if there already exists a qualitative framework, all the relevant nodes and arrows have been identified, even in the absence of figures.

8 Only then is it clear which CPTs to create. The qualitative framework presented here has been developed following the methodology given by Lagerstr m et al [14]. The theory proposed in this paper consists only of positive or negative causal effects between variables, and indications of the strength of the relations. These relations should be represented in the EID so that the framework can be put to use in future empirical studies to improve the model. Thus, we use the following qualitative relations inspired by Chung et al. [17]: 1. AND. The and relation reflects a relation where two or more quantities all need to be present for another quantity to emerge. This is denoted by an arc connecting the relevant influence arrows.

9 2. OR. The or relation reflects a relation where just one out of two or more quantities need to be present for another quantity to emerge. This is denoted by two arcs connecting the relevant influence arrows. 3. ENABLES. The enables relation expresses a strong positive influence of one quantity on another one. This is denoted by ++. 4. SUPPORTS. The supports relation expresses a positive influence of one quantity on another one. This is denoted by +. 5. UNDERCUTS. The undercuts relation expresses a negative influence of one quantity on another one. This is denoted by -. 6. DISABLES. The disables relation expresses a strong negative influence of one quantity on another one. This is denoted by --. Fig. 2. Relations between quantities, used in qualitative modeling.

10 Proceedings of BUSITAL 08 47 To understand how these qualitative relations work, consider Fig. 3. Using the relations specified above we can refurnish a diagram, such as the simple AND-example below, into a set of tentative CPTs, where the figures reflect the relations used. While these CPTs are somewhat arbitrary both the (a) and the (b) alternatives are acceptable representations they are well-defined in the sense that CPTs such as (c) are clearly unacceptable. Furthermore, these CPTs can be updated in a non-arbitrary fashion, using the well-known learning algorithms of Bayesian networks described for instance by Jensen [13], whenever empirical data is available.